Respiratory Motion Modelling Using cGANs

被引：12

作者：

Giger, Alina ^{[1
]}

Sandkuhler, Robin ^{[1
]}

Jud, Christoph ^{[1
]}

Bauman, Grzegorz ^{[1
,2
]}

Bieri, Oliver ^{[1
,2
]}

Salomir, Rares ^{[3
]}

Cattin, Philippe C. ^{[1
]}

机构：

[1] Univ Basel, Dept Biomed Engn, Allschwil, Switzerland

[2] Univ Hosp Basel, Dept Radiol, Div Radiol Phys, Basel, Switzerland

[3] Univ Geneva, Image Guided Intervent Lab, Geneva, Switzerland

来源：

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV | 2018年 / 11073卷

基金：

瑞士国家科学基金会;

关键词：

Respiratory motion model; 4D MRI; cGAN; NEURAL-NETWORK; ORGAN MOTION; PREDICTION;

D O I：

10.1007/978-3-030-00937-3_10

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Respiratory motion models in radiotherapy are considered as one possible approach for tracking mobile tumours in the thorax and abdomen with the goal to ensure target coverage and dose conformation. We present a patient-specific motion modelling approach which combines navigator-based 4D MRI with recent developments in deformable image registration and deep neural networks. The proposed regression model based on conditional generative adversarial nets (cGANs) is trained to learn the relation between temporally related US and MR navigator images. Prior to treatment, simultaneous ultrasound (US) and 4D MRI data is acquired. During dose delivery, online US imaging is used as surrogate to predict complete 3D MR volumes of different respiration states ahead of time. Experimental validations on three volunteer lung datasets demonstrate the potential of the proposed model both in terms of qualitative and quantitative results, and computational time required.

引用

页码：81 / 88

页数：8

共 12 条

[1] Ultra-fast steady state free precession and its application to in vivo 1H morphological and functional lung imaging at 1.5 tesla [J].